Mining of signed networks where the links/edges between nodes have a positive or negative sign/label, is getting the attention of researchers and practitioners due to its wide realworld applicability in various domains. Label prediction for nodes in a network is a well-known and explored problem. However, the prediction of labels for edges in a network is relatively less explored, and very challenging and interesting problem. In this paper, we consider the problem of binary label prediction for edges in undirected and unweighted networks. The prediction of binary labels has a number of applications in realworld like friend/foe prediction, recommendation, trust/distrust prediction in social networks, and categorization. In this work, a semi-supervised approach is proposed that uses the concept of random-walk for prediction of binary labels for edges. In this paper, we demonstrate the viability and the effectiveness of the proposed approach using a real-world network.

Prediction of Binary Labels for Edges in Signed Networks: A Random-Walk Based Approach

Automating repetitive processes and replacing manual tasks with automated systems is an area of research that will greatly impact and transform our lives during the 21st century. Automation comes in many forms and we are now at the start of an era, after which repetitive non-creative tasks will be handled mainly by machines. In this thesis, two analytics approaches are presented that can be used to automate text processing tasks. The first is an automation approach using machine learning in which we show how we can improve text classification performance, and how we, through these improvements, can reach practically acceptable performance levels even in certain abstract classification problems. We test the developed methods on problematic web content categories, such as violence, racism, and hate. The second is an automation approach that uses network analytics to automatically process texts. We use this approach to automate processing of financial news and to automatically extract new information. We show that through automating the process, we can extract company specific sentimentrisks that a person would not identify simply by reading the news articles. Lastly, we show that the risks we have extracted can be used to identify companies that are at higher risk of stock price decrease.

Automating Text Processing Using Analytics - Automating text classifications and financial news parsing

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/07/2015026-陈卫-社交网络影响力传播研究（最终版）-1.pdf

社交网络影响力传播研究 (Research on influence diffusion in social networks)

In this paper we have proposed the clustering approach to classify the random walk trajectories from the synthetic bus station video. Bus station one of the most crowded locations that consist of more than thousands of passengers or travelers waiting for the buses to travel to the destination point. These crowded locations can be highly prone to accidents or terrorist activities. Work is classified into two steps i.e Firstly we find out the trajectories from the image by using the machine learning approach after that we apply the agglomerative clustering algorithm which is used to group the abnormal trajectories with the similar spatial patterns and normal trajectories with similar spatial patterns. Keywords—Path detection, Anomaly Detection, Trajectories,

/pdf/passenger-abnormal-behaviour-detection-using-machine-yr26u1g3cd.pdf

Passenger Abnormal Behaviour Detection using Machine Learning Approach

Social Network Data Analytics || An Introduction to Social Network Data Analytics

The Numerical Weather Prediction (NWP) models, Global Forecast System (GFS) and Weather Research & Forecast [WRF(ARW)] operationally run by India Meteorological Department (IMD) have been utilized to estimate sub-basin-wise rainfall forecast. The sub-basin-wise operational Quantitative Precipitation Forecast (QPF) have been issued by 14 Flood Meteorological Offices (FMOs) of IMD located at different flood prone areas of the country. The daily sub-basin-wise NWP model rainfall forecast for 146 sub-basins under these 14 FMOs for the flood season 2017 & 2018 and for 153 sub-basins for the flood season 2019 have been estimated on operational basis which are used by forecasters at FMOs as a guidance for the issue of operational sub-basin QPF for flood forecasting purposes. The performance of GFS and WRF (ARW) models in respect of rainfall at the sub-basin level have been studied in detail. It is found that the critical success index (CSI) and Probability of Detection (POD) decrease from lower to higher category of rainfall whereas False Alarm Rate (FAR) increases from lower to higher categories. The case base heavy rainfall analysis showed that model generally underestimated the rainfall. It is also found that performance of GFS is little better than WRF (ARW) when compared over all the flood prone river sub basins of India. It is also found that the performance of WRF (ARW) is little better in hilly areas in comparison to GFS.

Evaluation of performance of WRF (ARW) and GFS for Quantitative Precipitation Forecast and its value addition over India river sub-basins during recent years

C Charu

Papers

Social Network Data Analytics || An Introduction to Social Network Data Analytics

Evaluation of performance of WRF (ARW) and GFS for Quantitative Precipitation Forecast and its value addition over India river sub-basins during recent years